SAP Hybris: IoT and Machine Learning in The Retail Supply Chain

Campbell's Soup I Full Suite (Andy Warhol, 1968)

The World is Drowning in Data

The business world is drowning in data. And much of this data is generated, consumed, and managed by SAP enterprise software systems.

At the recent SAP Hybris Global Summit in Barcelona, SAP described how 76% of enterprise data worldwide flows through data pipes and databases managed by SAP enterprise software systems. According to SAP, its top 10 customers drive more revenue from these data systems than IBM and Oracle Demantra software combined

One of the clichés often heard in big data analytics conferences is “data is the new fuel of the enterprise” (although I don’t think I heard it said in Barcelona). But how, exactly, can organizations handle the torrent of data from the vast array of new and traditional sources remains a challenge. How to convert voluminous structured and unstructured data into business fuel that drives high-fidelity decisions and better business outcomes is quite murky and elusive.

SAP Hybris believes it has the answer.

Organizational Myopia

Scarcity of up-to-date, comprehensive, high-quality business data, and poor ability to manage, analyze and act upon the meager data that is available can lead to acute organizational myopia: Once a product is sold or installed in the field, most organizations lose sight of the prodcut’s performance, how users are interacting with it, and how well it meets customers’ expectations and business goals.

Worse, when new data does arrive and provides a glimpse into the customer’s experience, it’s usually in the form of bad news: customer complaints, warranty claims, and expensive repair or replacement.

The Internet of Things and the digitization of the product value chain can help product and service organizations overcome much of this organization’s myopia. IoT-enabled business processes radically redefine how well the enterprise understands current products and customers, and how it uses this insight to accelerate and improve the innovation and development of new products and services.

Unleashing the Power of Data

The call for enhancing operational visibility and boosting business agility isn’t new, of course. But the understanding and implementation of business agility practices in the pre-digital age pale in comparison to business demands in the 21st century.

Hyper-connectivity between people, processes, data, and things (as in “the Internet of Things”) is challenging traditional data management practices and processes. Once siloed by lines of business, data repositories are being connected and consolidated, and integrated business platforms aggregate and share data across enterprise business functions and user groups.

Today’s connected world, in which everyone and everything is connected around-the-clock, allow product companies to better understand what their customers do and need on a real-time basis.

This persistent transparency enables brand owners to be super in-tune with their customers, one at a time, and drive product improvement and customer satisfaction to new levels. It lets manufacturers recalibrate their manufacturing and supply chains and become highly efficient and competitive.

Humans as sensors

Big data is not some amorphous technical concept isolated from society. Until a decade ago, most of the world’s data was created by scientific, industrial and administrative sources.  But today, the volume and velocity of data generated by millions of daily human interactions worldwide has exceeded that of corporate-generated data.

Connected users and social media interactions provide ongoing feedback and early warning signs not only about product failures, quality defects, and software bugs, but are often eye-opening testimonials about general attitudes towards product features and usability, and the strength of the brand.

In a digitized and connected world, customers are not only consumers of information, they are also producers of information and insight. In the always-connected world of IoT, people, too, are “things”: they act as sensors that provide relentless, diverse and candid feedback about products, support operations, and the image of the brand.

And, in fact, these red flags are frequently visible sooner and are louder than the information available to the organization through traditional channels and tedious analyses of support tickets and warranty claims.

Signal, Noise and Bias

The notion of harvesting value from data is very promising. But these big data lakes are so voluminous, convoluted, and fast-moving, that separating the wheat from the chaff can be difficult and time-consuming, and applying findings and lessons in the context of always-changing business needs is often slow and less effective.

Moreover, Institutional data repositories may not incorporate sufficient diversity of patterns, and frequently include invalid instances of bad habits and poor decision patterns that have lingered undetected in the organization for years. Worse, enterprise data, and, more so, external data such as social media, is very likely to include biases, whether intended or not. It’s easy to understand how datasets of human decisions may include cultural, educational, gender, race, and other biases, which will be propagated by self-learning systems and might result in incorrect actions and harmful discrimination.

Can SAP Capitalize on Its Strengths?

SAP’s apt tagline: Intelligently Connecting People, Things and Businesses, links key constituents: devices, people and businesses to the portfolio of technologies and business services. SAP Hybris omnichannel eCommerce platform and machine learning tools propose to leverage connectivity to deliver better insight and demand forecast, richer context for decision making, and superior user experience.

Enterprise information systems should provide a broad and rich multidisciplinary context by synthesizing data from multiple enterprise data systems and repositories. Sifting through and analyzing volumes of customer data, artificial intelligence systems can learn from experience, and couple this empirical knowledge with institutional policies and best practices, regulations, and additional transactional data. Machine learning algorithms can get a better signal from the noise to pinpoint trends and tailor responses and continually adapt and respond to new conditions as soon as they are detected.

The ability to connect systems to the enterprise’s digital backbone using SAP’s Leonardo IoT Platform and enrich them by augmenting information from other enterprise systems and data repositories gives SAP a clear advantage over mainstream IoT platforms that focus on connectivity and data flow.

The challenge facing SAP is to articulate the value of a powerful portfolio of technology and process building blocks, and simultaneously keep the message focused on business processes, business outcomes and customer value. This can be difficult, especially when the message needs to articulate the role of core technologies such as IoT and analytics, and, at the same time, establish Leonardo as an independent innovation platform. But it’s certainly doable.


Image: Campbell’s Soup I Full Suite (Andy Warhol, 1968)

  • johncrm72

    Really interesting read.